Since the first report of chemical exchange saturation transfer (CEST) contrast in 2000, this imaging technology has attracted many new research studies, resulting in a number of preclinical and now also clinical applications. Endogenous CEST contrast has been applied to characterizing acute ischemia and brain tumors, visualizing the concentration of tissue amide protons and their chemical exchange rate. CEST contrast has been found to relate to tumor grade, and allows separation of recurrent tumor from the effects of treatment. This contrast is also used in musculoskeletal imaging for monitoring glycosaminoglycan concentrations in cartilage. In addition, CEST reporter genes are being developed allowing detection of cells expressing this gene.
An important advantage of CEST is the capability to design agents with protons at different frequencies, allowing simultaneous detection of probes with different functions. CEST probes have been designed to label virus particles, allow imaging of the kidneys, and allow the detection of peptides, drug delivery particles, changes in temperature, pH, and metabolite concentrations. Ultimately, for both endogenous and exogenous CEST contrast agent studies, improved detection technologies will be important to speed up the transition to widespread preclinical and clinical use.
CEST contrast is produced through the application of a radiofrequency saturation pulse at the resonance frequency of the exchangeable protons, after which the resulting saturation is transferred via chemical exchange to bulk water leading to a loss in signal that yields contrast. However, the application of this pulse results in other sources of water signal loss, such as due to conventional magnetization transfer contrast (MTC, mainly from solid-like macromolecules in tissue) and direct saturation (DS), complicating image analysis. To analyze the sources of water signal loss, it is widespread practice to plot the saturated water signal intensity (S) normalized with the intensity without saturation (S0) as a function of saturation offset with respect to water, termed a Z-spectrum. As shown in FIGS. 1A-1C, the symmetries of the DS and MTC signals with respect to the water frequency (assigned to 0 ppm) differ from CEST. Because CEST contrast is asymmetric with respect to the water frequency, the conventional way to detect and quantify CEST contrast has been by calculating the asymmetry in the magnetization transfer ratio (MTRasym) at the frequency of the exchangeable protons(Δω):
                              MTR          asym                =                              (                                          s                ⁡                                  (                                      -                    Δω                                    )                                            -                              s                ⁡                                  (                                      +                    Δω                                    )                                                      )                                s            ⁢                                                  ⁢            0                                              (        1        )            
FIGS. 1A-1C illustrate simulations of the Z-spectra produced by solutions containing either CEST (PLL or L-arginine) or conventional MTC agents (agar) to display the symmetries of the various contributions to saturation signal loss. More particularly, FIG. 1A illustrates a Z-spectrum of PLL (solid line) in PBS and second without contrast agent (dash line). FIG. 1B illustrates a Z-spectrum for L-arginine (solid line) in PBS and second without contrast agent (dash line), and FIG. 1C illustrates a Z-spectrum for 2% Agar (solid line) in PBS and second without contrast agent (dash line).
The proton transfer ratio (PTR) is a metric used to describe CEST contrast for a certain proton type in a given agent. Unfortunately, the standard assumption that the experimentally determined MTRasym equals PTR is not valid as MTC may have an inherent asymmetric component (MTRasyminherent). Moreover, the spatial inhomogeneity of magnetic field results in water frequency variations and produces artifacts (MTRasymfield). Therefore, the experimentally measured asymmetry is given by:MTRasym=PTR+MTRasymfield+MTRasyminherent)   (2)
Errors in MTRasym due to MTRasymfield contributions can be reduced by mapping the field and performing a voxel-based offset correction, which are categorized as offset incrementation correction (OIC) methods. Mapping the field can be accomplished through fitting the Z-spectrum for each pixel or through gradient echo based methods or fitting Z-spectra acquired using short, weak saturation pulses. The corrected contrast map is generated by acquiring a reduced number of images with frequencies around the proton of interest. Such types of either partial or whole Z-spectra acquisition require relatively long scan times and have the disadvantage that the CNR of the contrast map does not increase as the number of offsets and the scan time increase. To partially compensate for this, CEST contrast can also be calculated by integrating over the width of the CEST peak or using a Lorentzian line-fitting, but still require sweeping the offset over a wide range. Recently an additional method has been proposed which utilizes two saturation frequency alternating to cancel out the MTRasymfield and MTRasyminherent.
It would therefore be advantageous to provide a method of MRI which an aspect of the saturation pulse is varied to modulate the water signal loss, such as using cosine modulation and impart differential phases on the three different components of the asymmetric MTR contributions (PTR, (tsat), and (Δω)). This allows their separation using post-processing techniques similar to those for analyzing time-varying signals in fMRI and other imaging moiety, such as the general linear model (GLM) to identify modulation patterns, fast fourier transform (FFT) to separate different frequency components or pattern recognition method, such as principal component analysis, independent component analysis and fuzzy analysis.